Cargando…
Machine learning implicates the IL-18 signaling axis in severe asthma
Asthma is a common disease with profoundly variable natural history and patient morbidity. Heterogeneity has long been appreciated, and much work has focused on identifying subgroups of patients with similar pathobiological underpinnings. Previous studies of the Severe Asthma Research Program (SARP)...
Autores principales: | Camiolo, Matthew J., Zhou, Xiuxia, Wei, Qi, Trejo Bittar, Humberto E., Kaminski, Naftali, Ray, Anuradha, Wenzel, Sally E. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Clinical Investigation
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8663569/ https://www.ncbi.nlm.nih.gov/pubmed/34591794 http://dx.doi.org/10.1172/jci.insight.149945 |
Ejemplares similares
-
Expression of SARS-CoV-2 receptor ACE2 and coincident host response signature varies by asthma inflammatory phenotype
por: Camiolo, Matthew, et al.
Publicado: (2020) -
Should lung biopsies be performed in patients with severe asthma?
por: Doberer, Daniel, et al.
Publicado: (2015) -
Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning
por: Ray, Anuradha, et al.
Publicado: (2022) -
Using ICLite for deconvolution of bulk transcriptional data from mixed cell populations
por: Camiolo, Matthew J., et al.
Publicado: (2021) -
Implication of IL-18 in chronic inflammation of severe refractory asthma
por: Rovina, Nikoletta, et al.
Publicado: (2015)